A Review of Single and Population-Based Metaheuristic Algorithms Solving Multi Depot Vehicle Routing Problem

Abstract

Multi-Depot Vehicle Routing Problem (MDVRP) arises with rapid development in the logistics and transportation field in recent years. This field, mainly, faces challenges in arranging their fleet efficiently to distribute the goods to customers by minimizing distance and cost. Therefore, the decision maker needs to specify the vehicles to reach the particular depot which, serves the customers with the predetermined capacity. Hence, to solve the stated problems, there is a need to apply metaheuristic methods to get minimal transportation costs. This article reviews on single and population-based metaheuristic methods solving MDVRP from the year 2013 until 2018. The methods discussed were simulated annealing (SA), variable neighborhood search (VNS), ant colony algorithm (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). From the previous works, it can be concluded that the application of populationbased metaheuristic gives better solutions in solving MDVRPs.

Regional Location Routing Problem for Waste Collection Using Hybrid Genetic Algorithm-Simulated Annealing

“…Moreover, GA has an advantage in providing faster computational time, but it is not easy to obtain the optimum value, so we increase the population for GA. In contrast, SA may take a longer computational process since the initial solution is randomized [28]. Therefore, the GA outputs are upgraded using SA to exploit and produce a better solution.…”

Data Fusion Approach for Managing Clinical Data in an Industrial Environment using IoT

“…ACO has the ability to cluster and construct routes, and PSO is simple to implement. However, due to its poor exploration, PSO has issues with parameter selection [31]. First, according to the characteristics of physiological data d; the highest sum combination ensures that the model works with the optimized parameters to improve the accuracy of disease classification prediction.…”

Imperialist Competitive Algorithm with Independence and Constrained Assimilation

“…Vehicles in the MDVRP are subject to capacity constraints (how much cargo can be carried on board) and the maximum duration for the route before the vehicle needs to return to the original depot. The MDVRP resembles a lot of everyday transportation, logistics and distribution problems and, therefore, has been a common research area [46]. Furthermore, the MDVRP is also an NP-hard combinatorial optimisation problem; thus, optimal solutions are hard to find [47].…”

“…Although exact algorithms for solving these classes of problems exist, they are limited to small problem instances [48]. A wide range of metaheuristics and population-based algorithms have been used [46] to solve larger instances of the MDVRP.…”

“…They have been a growing interest in many areas [57], and MDVRP is no exception. A recent survey of metaheuristic algorithms [46] suggests that two of the most common algorithms used for solving MDVRP are Ant Colony Optimization (ACO) and Genetic Algorithm (GA). However, other algorithms like Particle Swarm Optimization (PSO) [58] and Ant Lion Optimization (ALO) [59] have also been successfully applied.…”